Environmental Science and Pollution Research

, Volume 26, Issue 30, pp 31014–31025 | Cite as

Impact of environmental variables on the efficiency of water companies in England and Wales: a double-bootstrap approach

  • Andres Villegas
  • María Molinos-SenanteEmail author
  • Alexandros Maziotis
Research Article


An important aspect of the regulatory process is the performance comparison of regulated firms. This exists in regulated industries where tariffs are determined through a benchmarking process such as the English and Welsh water industry. A double-bootstrap data envelopment analysis (DEA) approach was applied to overcome the uncertainty in efficiency scores and to reveal the influence of environmental variables on 18 water companies in England and Wales during the 2001–2016 period. The results showed that bias and bias-corrected efficiency scores lead to changes in the water companies’ rankings. This reveals the importance of using reliable methodologies to support the decision-making process. Higher levels of average pumping head, leakage, and abstraction of water from reservoirs lead to lower efficiency. In contrast, increased population density leads to larger efficiency. We also link the results from the efficiency of water companies with the regulatory cycle. Our findings can be useful to policy makers for them to better understand water utilities’ performance and to aid them in reshaping their current policies and practices to improve efficiency and provide better service to customers.


DEA Double-bootstrap Environmental variables Performance Water utilities English and Wales 


Supplementary material

11356_2019_6238_MOESM1_ESM.docx (39 kb)
ESM 1 (DOCX 39 kb)


  1. Ananda J (2014) Evaluating the performance of urban water utilities: robust nonparametric approach. J Water Resour Plan Manag 140(9):04014021CrossRefGoogle Scholar
  2. Ananda J, Pawsey N (2019) Benchmarking service quality in the urban water industry. J Prod Anal 51:55–72CrossRefGoogle Scholar
  3. Ashton JK (2000) Cost efficiency in the UK water and sewerage industry. Appl Econ Lett 7(7):455–458CrossRefGoogle Scholar
  4. Badin L, Daraio C, Simar L (2014) Explaining inefficiency in nonparametric production models: the state of the art. Ann Oper Res 214(1):5–30CrossRefGoogle Scholar
  5. Berg S, Lin C (2008) Consistency in performance rankings: the Peru water sector. Appl Econ 40(6):793–805CrossRefGoogle Scholar
  6. Berg S, Marques R (2011) Quantitative studies of water and sanitation utilities: a benchmarking literature survey. Water Policy 13(5):591–606CrossRefGoogle Scholar
  7. Bogetoft P, Otto L (2011) Benchmarking with DEA, SFA, and R. Springer, LondonCrossRefGoogle Scholar
  8. Bottasso A, Conti M (2003) Cost inefficiency in the English and Welsh water industry: an heteroskedastic stochastic cost frontier approach. DIEM Universita` di Genova, mimeoGoogle Scholar
  9. Bottasso A, Conti M (2009) Scale economies, technology and technical change in the water industry: evidence from the English water only sector. Reg Sci Urban Econ 39(2):138–147CrossRefGoogle Scholar
  10. Brea-Solis H, Perelman S, Saal DS (2017) Regulatory incentives to water losses reduction: the case of England and Wales. J Prod Anal 47(3):259–276CrossRefGoogle Scholar
  11. Byrnes J, Crase L, Dollery B, Villano R (2010) The relative economic efficiency of urban water utilities in regional new South Wales and Victoria. Resour Energy Econ 32(3):439–455CrossRefGoogle Scholar
  12. Carvalho P, Marques RC (2011) The influence of the operational environment on the efficiency of water utilities. J Environ Manag 92(10):2698–2707CrossRefGoogle Scholar
  13. Carvalho P, Marques RC (2014) Computing economies of vertical integration, economies of scope and economies of scale using partial frontier nonparametric methods. Eur J Oper Res 234(1):292–307CrossRefGoogle Scholar
  14. Carvalho P, Marques RC (2016) Computing economies of scope using robust partial frontier nonparametric methods. Water (Switzerland) 8(3):82Google Scholar
  15. CEPA (2011) Cost assessment - use of panel and sub-company data. Cambridge Economic Policy Associates Ltd, Report prepared for OfwatGoogle Scholar
  16. CEPA (2014) Cost assessment—advanced econometric models. Cambridge Economic Policy Associates Ltd, Report prepared for OfwatGoogle Scholar
  17. Charnes A, Cooper WW, Rhodes E (1978) Evaluating program and managerial efficiency: an application of data envelopment analysis to program follow through. Manag Sci 27(6):668–697CrossRefGoogle Scholar
  18. Corton ML, Berg SV (2009) Benchmarking central American water utilities. Util Policy 17(3–4):267–275CrossRefGoogle Scholar
  19. Da Cruz NF, Marques RC (2014) Revisiting the determinants of local government performance. Omega (United Kingdom) 44:91–103Google Scholar
  20. De Witte K, Marques RC (2010) Designing performance incentives, an international benchmark study in the water sector. CEJOR 18(2):189–220CrossRefGoogle Scholar
  21. De Witte K, Marques RC (2011) Gaming in a benchmarking environment. A non-parametric analysis of benchmarking in the water sector. Water Policy 14(1):45–66CrossRefGoogle Scholar
  22. Du K, Worthington AC, Zelenyuk V (2018) Data envelopment analysis, truncated regression and double-bootstrap for panel data with application to Chinese banking. Eur J Oper Res 265(2):748–764CrossRefGoogle Scholar
  23. Färe R, He X, Li S, Zelenyuk V (2019) A unifying framework for farrell profit efficiency measurement. Oper Res 67(1):183–197CrossRefGoogle Scholar
  24. Ferreira DC, Marques RC (2017) A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale. Oper Res, 1-36Google Scholar
  25. Gidion DK, Hong J, Adams MZA, Khoveyni M (2019) Network DEA models for assessing urban water utility efficiency. Util Policy 57:48–58CrossRefGoogle Scholar
  26. Gocht A, Balcombe K (2006) Ranking efficiency units in DEA using bootstrapping an applied analysis for Slovenian farm data. Agric Econ 35:223–229CrossRefGoogle Scholar
  27. Guerrini A, Romano G, Campedelli B (2013) Economies of scale, scope, and density in the Italian water sector: a two-stage data envelopment analysis approach. Water Resour Manag 27(13):4559–4578CrossRefGoogle Scholar
  28. Güngör-Demirci G, Lee J, Keck J (2017) Measuring water utility performance using nonparametric linear programming. Civ Eng Environ Syst 34(3-4):206–220CrossRefGoogle Scholar
  29. Güngör-Demirci G, Lee J, Keck J (2018) Assessing the performance of a California water utility using two-stage data envelopment analysis. J Water Resour Plan Manag 144(4):05018004aCrossRefGoogle Scholar
  30. Lee BL, Worthington AC (2014) Technical efficiency of mainstream airlines and low-cost carriers: new evidence using bootstrap data envelopment analysis truncated regression. J Air Transp Manag 38:15–20CrossRefGoogle Scholar
  31. Lepine A, Vassall A, Chandrashekar S (2015) The determinants of technical efficiency of a large scale HIV prevention project: application of the DEA double bootstrap using panel data from the Indian Avahan. Cost Eff Resour Alloc 13(1):5CrossRefGoogle Scholar
  32. Marques RC (2008) Comparing private and public performance of Portuguese water services. Water Policy 10(1):25–42CrossRefGoogle Scholar
  33. Marques RC, Berg S, Yane S (2014) Nonparametric benchmarking of Japanese water utilities: institutional and environmental factors affecting efficiency. J Water Resour Plan Manag 140(5):562–571CrossRefGoogle Scholar
  34. Marques RC, Simoes P, Pinto FS (2018) Tariff regulation in the waste sector: an unavoidable future. Waste Manag 78:292–300CrossRefGoogle Scholar
  35. Maziotis A, Saal DS, Thanassoulis E, Molinos-Senante M (2016) Price cap regulation in the English and Welsh water industry: a proposal for measuring performance. Util Policy 41:22–30CrossRefGoogle Scholar
  36. Maziotis A, Molinos-Senante M, Sala-Garrido R (2017) Assessing the impact of quality of service on the productivity of water industry: a Malmquist-Luenberger approach for England and Wales. Water Resour Manag 31:2407–2427CrossRefGoogle Scholar
  37. Molinos-Senante M, Maziotis A (2017) Estimating economies of scale and scope in the English and Welsh water industry using flexible technology. J Water Resour Plan Manag 143(10):04017060CrossRefGoogle Scholar
  38. Molinos-Senante M, Maziotis A (2018) Assessing the influence of exogenous and quality of service variables on water companies´ performance using a true-fixed stochastic frontier approach. Urban Water J 15(7):682–691CrossRefGoogle Scholar
  39. Molinos-Senante M, Maziotis A (2019) Cost efficiency of English and Welsh water companies: a meta-stochastic frontier analysis. J Water Resour Manag 33:3041–3055. CrossRefGoogle Scholar
  40. Molinos-Senante M, Maziotis A, Sala-Garrido R (2014) The Luenberger productivity indicator in the water industry: an empirical analysis for England and Wales. Util Policy 30:18–28CrossRefGoogle Scholar
  41. Molinos-Senante M, Maziotis A, Mocholi-Arce M, Sala-Garrido R (2015) Accounting for service quality to customers in the efficiency of water companies: evidence from England and Wales. Water Policy 18(2):513–532Google Scholar
  42. Molinos-Senante M, Maziotis A, and Sala-Garrido R (2017) “Assessing the productivity change of water companies in England and Wales: A dynamic metafrontier approach”. J Environ Mang 197(15):1–9Google Scholar
  43. Molinos-Senante M, Porcher S, Maziotis A (2018a) Impact of regulation on English and Welsh water-only companies: an input distance function approach. Environ Sci Pollut Res 24(20):16994–17005CrossRefGoogle Scholar
  44. Molinos-Senante M, Donoso G, Sala-Garrido R, Villegas A (2018b) Benchmarking the efficiency of the Chilean water and sewerage companies: a double-bootstrap approach. Environ Sci Pollut Res 25(9):8432–8440CrossRefGoogle Scholar
  45. Molinos-Senante M, Maziotis A, Sala-Garrido R (2019) Estimating profit, price, and productivity changes in water industry using Bennet-Bowley indicator. J Water Resour Plan Manag 145(5):04019011–04019011CrossRefGoogle Scholar
  46. Ofwat (2009) Future water and sewerage charges 2010–2015. Final determinations, Office of Water Services, BirminghamGoogle Scholar
  47. Oxera (2007) Assessing approaches to expenditure and incentives. Report Prepared for Ofwat, OxfordGoogle Scholar
  48. Pinto FS, Simões P, Marques RC (2017) Water services performance: do operational environment and quality factors count? Urban Water J 14(8):773–781CrossRefGoogle Scholar
  49. Porcher S, Maziotis A, Molinos-Senante M (2017) The welfare costs of non-marginal water pricing: evidence from the water only companies in England and Wales. Urban Water J 14(9):947–953CrossRefGoogle Scholar
  50. Portela MCAS, Thanassoulis E, Horncastle A, Maugg T (2011) Productivity change in the water industry in England and Wales: application of the meta-Malmquist index. J Oper Res Soc 62(12):2173–2188CrossRefGoogle Scholar
  51. Romano G, Salvati N, Guerrini A (2018) Governance, strategy and efficiency of water utilities: the Italian case. Water Policy 20(1):109–126CrossRefGoogle Scholar
  52. Saal DS, Parker D (2000) The impact of privatization and regulation on the water and sewerage industry in England and Wales: a translog cost function model. Manag Decis Econ 21:253–268CrossRefGoogle Scholar
  53. Saal DS, Parker D (2001) Productivity and price performance in the privatized water and sewerage companies of England and Wales. J Regul Econ 20(1):61–90CrossRefGoogle Scholar
  54. Saal DS, Parker D (2004) The comparative impact of privatization and regulation on productivity growth in the English and Welsh water and sewerage industry, 1985-99. Int J Reg Gov 4(2):139–170Google Scholar
  55. Saal DS, Parker D (2006) Assessing the performance of water operations in the English and welsh water industry: a lesson in the implications of inappropriately assuming a common frontier. In: Coelli T, Lawrence D (eds) Performance measurement and regulation of network utilities. Edward Elgar, CheltenhamGoogle Scholar
  56. Saal DS, Parker D, Weyman-Jones T (2007) Determining the contribution of technical change, efficiency change and scale change to productivity growth in the privatized English and Welsh water and sewerage industry: 1985–2000. J Prod Anal 28(1–2):127–139CrossRefGoogle Scholar
  57. Saal, D.S., Arocena, P., Maziotis, A. (2011). The cost implications of alternative vertical configurations of the English and Welsh water and sewerage industry. ACCIS Working Paper, No. 8. Aston University.Google Scholar
  58. See KF (2015) Exploring and analysing sources of technical efficiency in water supply services: some evidence from Southeast Asian public water utilities. Water Resour Econ 9:23–44CrossRefGoogle Scholar
  59. Simar L, Wilson PW (2007) Estimation and inference in two-stage, semiparametric models of production processes. J Econ 136(1):31–64CrossRefGoogle Scholar
  60. Stone & Webster Consultants (2004) Investigation into evidence for economies of scale in the water and sewerage industry in England and Wales; Final Report, Report prepared for and published by OfwatGoogle Scholar
  61. UN (2019) United Nations´ Sustainable Development Goals. Available at:
  62. Wang Y, Zheng T, Zhao Y, Jiang J, Wang Y, Guo L (2013) Monthly water quality forecasting and uncertainty assessment via bootstrapped wavelet neural networks under missing data for Harbin, China. Environ Sci Pollut Res 20(12):8909–8923CrossRefGoogle Scholar
  63. Wijesiri M, Viganò L, Meoli M (2015) Efficiency of microfinance institutions in Sri Lanka: a two stage double bootstrap DEA approach. Econ Model 47:74–83CrossRefGoogle Scholar
  64. Xin K, Li F, Tao T, Xiang N, Yin Z (2015) Water losses investigation and evaluation in water distribution system – the case of SA city in China. Urban Water J 12(5):430–439CrossRefGoogle Scholar
  65. Zhao X, Ma C (2013) Deregulation, vertical unbundling and the performance of China’s large coal-fired power plants. Energy Econ 40:474–483CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Andres Villegas
    • 1
  • María Molinos-Senante
    • 2
    • 3
    Email author
  • Alexandros Maziotis
    • 4
    • 5
  1. 1.Departamento de Ingeniería ComercialUniversidad Técnica Federico Santa MaríaSantiagoChile
  2. 2.Departamento de Ingeniería Hidráulica y AmbientalPontificia Universidad Católica de ChileSantiagoChile
  3. 3.Centro de Desarrollo Urbano Sustentable CONICYT/FONDAP/15110020SantiagoChile
  4. 4.Foundazione Eni Enrico MatteiVeniceItaly
  5. 5.New York CollegeAthensGreece

Personalised recommendations